M. Anwar Ma'sum, H. Sanabila, W. Jatmiko, Aprinaldi
{"title":"基于多码本lvq的聚类人工神经网络","authors":"M. Anwar Ma'sum, H. Sanabila, W. Jatmiko, Aprinaldi","doi":"10.1109/ICACSIS.2015.7415193","DOIUrl":null,"url":null,"abstract":"In this paper we proposed multicodebook LVQ-based artificial neural network classifier using clustering approach. The classifiers are LVQ, LVQ2-1, GLVQ, and FNGLVQ. The clustering algorithm used to build multi codebook is K-Means, IK-Means, and GMM. Experiment result shows that on synthteic dataset multi codebook FNGLVQ using GMM clustering has higest improvement with 19,53% mprovement compared to FNGLVQ. Whereas on bencmark dataset multi codebook LVQ2-1 using K-Means clustering has higest improvement with 5,83% improvement cmpared to LVQ-2.1.","PeriodicalId":325539,"journal":{"name":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Multi codebook LVQ-based artificial neural network using clustering approach\",\"authors\":\"M. Anwar Ma'sum, H. Sanabila, W. Jatmiko, Aprinaldi\",\"doi\":\"10.1109/ICACSIS.2015.7415193\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we proposed multicodebook LVQ-based artificial neural network classifier using clustering approach. The classifiers are LVQ, LVQ2-1, GLVQ, and FNGLVQ. The clustering algorithm used to build multi codebook is K-Means, IK-Means, and GMM. Experiment result shows that on synthteic dataset multi codebook FNGLVQ using GMM clustering has higest improvement with 19,53% mprovement compared to FNGLVQ. Whereas on bencmark dataset multi codebook LVQ2-1 using K-Means clustering has higest improvement with 5,83% improvement cmpared to LVQ-2.1.\",\"PeriodicalId\":325539,\"journal\":{\"name\":\"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICACSIS.2015.7415193\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICACSIS.2015.7415193","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multi codebook LVQ-based artificial neural network using clustering approach
In this paper we proposed multicodebook LVQ-based artificial neural network classifier using clustering approach. The classifiers are LVQ, LVQ2-1, GLVQ, and FNGLVQ. The clustering algorithm used to build multi codebook is K-Means, IK-Means, and GMM. Experiment result shows that on synthteic dataset multi codebook FNGLVQ using GMM clustering has higest improvement with 19,53% mprovement compared to FNGLVQ. Whereas on bencmark dataset multi codebook LVQ2-1 using K-Means clustering has higest improvement with 5,83% improvement cmpared to LVQ-2.1.